# SmartTriage: A system for personalized patient data capture, documentation generation, and decision support

arXiv: https://arxiv.org/abs/2010.09905 · HTML: https://mlsanity.com/paper/2010.09905/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue](https://mlsanity.com/paper/2306.03652/) (2023)
- [A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records](https://mlsanity.com/paper/2303.08448/) (2023)
- [OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction](https://mlsanity.com/paper/2207.05817/) (2022)
- [Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach](https://mlsanity.com/paper/2210.02658/) (2022)
- [Dialogue-Contextualized Re-ranking for Medical History-Taking](https://mlsanity.com/paper/2304.01974/) (2023)

## Abstract (arXiv)

Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learning-backed system, SmartTriage, which goes beyond conventional symptom checking through a tight bi-directional integration with the electronic medical record (EMR). Conditioned on EMR-derived patient history, our system identifies the patient's chief complaint from a free-text entry and then asks a series of discrete questions to obtain relevant symptomatology. The patient-specific data are used to predict detailed ICD-10-CM codes as well as medication, laboratory, and imaging orders. Patient responses and clinical decision support (CDS) predictions are then inserted back into the EMR. To train the machine learning components of SmartTriage, we employed novel data sets of over 25 million primary care encounters and 1 million patient free-text reason-for-visit entries. These data sets were used to construct: (1) a long short-term memory (LSTM) based patient history representation, (2) a fine-tuned transformer model for chief complaint extraction, (3) a random forest model for question sequencing, and (4) a feed-forward network for CDS predictions. In total, our system supports 337 patient chief complaints, which together make up >90% of all primary care encounters at Kaiser Permanente.